DNA microarrays allow us to monitor thousands of genes simultaneously. One area of interest is oncology, where we want to predict the response of a patient to a certain compound, using the microarray data. The burden of microarrays is the fact that the number of variables (p) is much larger than the number of subjects (n). In this thesis, we compare existing large scale prediction methods, such as Supervised Principal Component Analysis and Lasso, to our newly developed method called Weigted Ensemble Prediction. The method removes the uninformative genes before applying the prediction method. After a univariate screening of the genes using correlations, we select subsets of genes on which we apply the prediction method. After X times we collect all the fitted models and record which genes where used regularly in the models. These genes are then used for the final prediction step. We will show that this methods gives more accurate predictions compared to the existing methods.